Common and specific cognitive deficits in schizophrenia: relationships to function

Cogn Affect Behav Neurosci (2014) 14:161–174
DOI 10.3758/s13415-013-0211-5
Common and specific cognitive deficits
in schizophrenia: relationships to function
Julia M. Sheffield & James M. Gold & Milton E. Strauss &
Cameron S. Carter & Angus W. MacDonald III & J. Daniel Ragland &
Steven M. Silverstein & Deanna M. Barch
Published online: 14 September 2013
# Psychonomic Society, Inc. 2013
Abstract The goals of the present study were to assess the
interrelationships among tasks from the MATRICS and
CNTRACS batteries, to determine the degree to which tasks
from each battery capture unique variance in cognitive dysfunction in schizophrenia, and to determine the ability of tasks
from each battery to predict functional outcome. Subjects
were 104 schizophrenia patients and 132 healthy control
subjects recruited as part of the CNTRACS initiative. All
subjects completed four CNTRACS tasks and two tasks from
the MATRICS battery: Brief Assessment of Cognition in
Schizophrenia Symbol Coding and the Hopkins Verbal Learning Test. Functional outcome was also assessed in the schizophrenia subjects. In both the patient and control groups, we
found significant intercorrelations between all higher order
cognitive tasks (episodic memory, goal maintenance, processing speed, verbal learning) but minimal relationships with the
visual task. For almost all tasks, scores were significantly
related to measures of functional outcome, with higher associations between CNTRACS tasks and performance-based
measures of function and between one of the MATRICS tasks
and self-reported functioning, relative to the other functioning
measures. After regressing out variance shared by other tasks,
we continued to observe group differences in performance
among task residuals, particularly for measures of episodic
memory from both batteries, although these residuals did not
correlate as robustly with functional outcome as raw test
scores. These findings suggest that there exists both shared
and specific variance across cognitive tasks related to cognitive and functional impairments in schizophrenia and that
measures derived from cognitive neuroscience can predict
functional capacity and status in schizophrenia.
Electronic supplementary material The online version of this article
(doi:10.3758/s13415-013-0211-5) contains supplementary material,
which is available to authorized users.
Keywords Cognitive control . Schizophrenia
J. M. Sheffield (*) : D. M. Barch
Department of Psychology, Washington University in St Louis,
1 Brookings Dr., St Louis, MO 63130, USA
e-mail: [email protected]
Introduction
J. M. Gold
Maryland Psychiatric Research Center, Catonsville, MD, USA
M. E. Strauss
Case Western Reserve University, Cleveland, OH, USA
C. S. Carter : J. D. Ragland
University of California at Davis, Davis, CA, USA
A. W. MacDonald III
University of Minnesota, Minneapolis, MN, USA
S. M. Silverstein
Rutgers – The State University of New Jersey, 57 U.S. Highway 1,
New Brunswick, NJ 08901, USA
Patients with schizophrenia experience deficits across a variety of cognitive domains (Elvevag & Goldberg, 2000; Gold &
Weinberger, 1995). It has been shown that these cognitive
impairments have a negative impact on patients’ ability to
function (Bowie et al., 2008; Green, 1996), contributing to
schizophrenia’s status as one of the leading causes of disability
in the United States (Ormel et al., 2008). Therefore, cognition
in schizophrenia has emerged as an important target for treatment development (Gray & Roth, 2007; McGurk, Twamley,
Sitzer, McHugo, & Mueser, 2007). In response to this need,
various stakeholder groups, including researchers, the NIMH,
industry, and the FDA, started two initiatives, both of which
resulted in batteries of cognitive paradigms that assess
162
multiple domains of cognition: the Measurement and Treatment Research to Improve Cognition in Schizophrenia
(MATRICS) battery (Kern et al., 2008; Nuechterlein et al.,
2008) and the Cognitive Neuroscience Test Reliability and
Clinical Applications for Schizophrenia (CNTRACS) consortium battery (Barch et al., 2012; Henderson et al., 2012;
Ragland et al., 2012; Silverstein et al., 2012) that followed
from the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) initiative. To
provide researchers with more information about the characteristics and utility of tasks from the CNTRACS and the
MATRICS batteries, the goals of the present study were to
assess the interrelationships among tasks from these batteries,
to determine the degree to which tasks from each battery
capture unique variance in cognitive dysfunction in schizophrenia, and to examine the ability of tasks from each battery
to predict functional outcome.
We are very pleased to put forth these results from the
CNTRACS work as part of this special issue honoring Ed
Smith. Ed Smith played an important role in helping to spawn
the CNTRICS initiative. He was part of the RAND panel that
helped to select tasks to be included in the MATRICS battery
and expressed his frustration that few paradigms developed as
part of modern cognitive neuroscience could be considered for
the MATRICS battery because of the absence of psychometric
data and the lack of standardization and optimization for use in
clinical populations. During this panel, he clearly voiced the
need for the field to put effort into the translation and psychometric development of paradigms developed as part of cutting
edge cognitive neuroscience research, which helped spur
Deanna Barch, Cam Carter, and others to start the CNTRICS
initiative. A perhaps little known fact about Ed Smith is that he
started and ended his career with a focus on schizophrenia,
bringing full circle an extraordinary career spent elucidating
the psychological and neural mechanisms that allow humans
to control their thoughts and actions. Given that schizophrenia
centrally involves deficits in cognitive control, working memory, and episodic memory, it is fitting that the final stages of
Ed Smith’s career involved applying the insights he developed
to help us understand how to characterize and treat cognitive
deficits in schizophrenia.
Relationship of constructs assessed by CNTRACS tasks
to MATRICS battery
The MATRICS and CNTRACS batteries were each developed through a consensus process, but with somewhat different conceptual approaches. The MATRICS battery needed to
be developed in a short time frame and focused on identifying
tasks with already established psychometric properties
(Kern et al., 2008; Nuechterlein et al., 2008). This necessitated the inclusion of more traditional and primarily
Cogn Affect Behav Neurosci (2014) 14:161–174
neuropsychological tasks, such as the Brief Assessment of
Cognition in Schizophrenia (BACS) Symbol-Coding test,
the Hamilton Verbal Learning Test, and a maze task, among
others. In contrast, the CNTRICS (Carter & Barch, 2007)
initiative focused on utilizing a contemporary cognitive
neuroscience-based approach, with the goal of identifying
and standardizing cognitive paradigms that were wellvalidated measures of specific cognitive mechanisms that
had, or were seen as amenable to, expression in animal models
that could be used in drug discovery and that could be used in
both early phase clinical trials and pathophysiology studies.
Also, the CNTRICS process included recognition that many
of the most promising tasks would need further modification,
standardization, and optimization of their psychometric properties before widespread use in clinical trials of schizophrenia
patients, a process that was not needed in MATRICS since
standardized clinical neuropsychology instruments were used.
Therefore, following on CNTRICS, the CNTRACS consortium developed a battery that assesses four component
processes from three cognitive domains: goal maintenance
(cognitive control), episodic memory (long-term memory),
visual integration (perception), and gain control in visual
processing (perception). Briefly, goal maintenance is the ability
to actively maintain contextual information that is important
for an ongoing task, such as task rules or previous stimuli. It is
distinct from short-term memory, in that the individual must
maintain a context, as opposed to the identity of a stimulus
(Servan-Schreiber, Cohen, & Steingard, 1996). Long-term
memory is the ability to store and appropriately retrieve previously presented information (Ragland et al., 2012). Visual
integration, or perceptual organization, is a process in which
pieces of visual information are integrated into a whole scene
or object and occurs one step beyond the registration of color,
orientation, motion, and depth (Silverstein et al., 2012). Gain
control is also a perceptual process and it refers to “processes
that amplify or attenuate overall levels of neural activity to
optimize operation of systems with limited dynamic signaling
range” (Barch et al., 2012, p. 135).
The motivation for the CNTRACS tasks was that they
would isolate specific perceptual or cognitive functions that
may be conflated in more traditional batteries, such as the
MATRICS, where task scores reflect the contribution of multiple cognitive processes (e.g., visual scanning, attention,
memory, problem solving, etc.). Therefore, the first goal of
the present study was to examine the relationships between
cognitive tasks from the CNTRACS and MATRICS batteries
and to assess the success of the CNTRACS paradigms in
isolating specific cognitive functions. Specifically, we hypothesized that we would see relatively small correlations with
tasks from the MATRICS battery, and we should see that the
CNTRACS tasks can identify variance in cognitive domains
in schizophrenia that may not be apparent on the MATRICS
tasks (and potentially, vice versa).
Cogn Affect Behav Neurosci (2014) 14:161–174
Relationship of CNTRACS and MATRICS tasks
to function in schizophrenia
As was described above, it has been shown that cognitive
impairments in schizophrenia have a negative impact on patients’ ability to function (Bowie et al., 2008; Green, 1996).
Thus, one goal in developing the MATRICS battery was for it
to predict functional status and for changes in MATRICS task
performance during treatment trials to predict change in
function. Consistent with these goals, the MATRICS composite score correlates strongly with the UCSD PerformanceBased Skills Assessment–Brief (UPSA–B), a performancebased measure of functional outcome (Keefe, Fox, Harvey,
Cucchiaro, Siu, & Loebel, 2011; Nuechterlein et al., 2008),
and also shows sensitivity to work status in schizophrenia
patients, with a “poor work group” performing significantly
worse on the battery than a “good work group” (August,
Kiwanuka, McMahon, & Gold, 2012). In the original development study, the MATRICS measures showed relatively
modest associations to self-reported function (Nuechterlein
et al., 2008), although this is perhaps not surprising, given
the evidence of limited validity for self-reports of function in
schizophrenia (Bowie et al., 2007). One of the goals in developing the CNTRACS battery was to test mechanisms of cognitive impairment and mechanisms of change. However, it is
also important to know whether the CNTRACS tasks relate to
function, to determine whether paradigms derived from basic
cognitive neuroscience are also relevant to understanding aspects of, and variability in, real-world functioning in schizophrenia. Clarification of these issues is also important for
addressing FDA requirements for functional “co-primary”
measures accompanying the use of cognitive task performance
as end points in registration studies. In a previous study, we
showed that the CNTRACS tasks assessing goal maintenance
and episodic memory correlated with the UPSA–B (Gold et al.,
2012). Furthermore, the CNTRACS goal maintenance task
was also correlated with informant reports of patient function, although not with self-reported function.
The results of these prior studies with the MATRICS and
CNTRACS suggest that both sets of tasks (or at least a subset)
show relationships to performance-based measures of functional outcome and some relationships to self-reported or
informant-reported outcome. Importantly, however, the magnitude of the reported relationships between functional outcome measures and the cognitive batteries differ, with the
MATRICS composite score showing a stronger relationship
with performance-based functioning than do the individual
CNTRACS tasks, in addition to slightly stronger relationships
with self-reported functioning (Gold et al., 2012; Keefe et al.,
2011; Nuechterlein et al., 2008). Although this pattern suggests that MATRICS tasks are better at predicting functional
outcome, this is difficult to compare across different patient
groups in different studies. As such, a second goal of the
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present study was to examine the relationships of the
CNTRACS tasks and a subset of the MATRICS tasks to
performance-based function and both self- and informantrated functional outcome in a new sample of patients. In
addition, we asked whether tasks from either battery correlated with function after accounting for performance on the other
task battery, to test the hypothesis that the tasks from each
battery would uniquely account for variance in function.
Common versus specific deficits in relationship
to function in schizophrenia
An ongoing debate in the literature is whether patients suffer
only from a single broad impairment that affects cognition in
schizophrenia (Dickinson, Iannone, Wilk, & Gold, 2004;
Dickinson, Ragland, Gold, & Gur, 2008) or whether there
are additional deficits in specific domains that provide
insight into the illness (Chapman & Chapman, 1978; Fornito,
Yoon, Zalesky, Bullmore, & Carter, 2011; Lesh, Niendam,
Minzenberg, & Carter, 2011; Repovs, Csernansky, & Barch,
2011). Prior work has shown strong intercorrelations among
all the MATRICS tasks (August et al., 2012), while only the
goal maintenance and episodic memory tasks from the
CNTRACS were moderately intercorrelated (Gold et al.,
2012). Thus, the literature clearly suggests that there is a
common deficit contributing to performance across tasks in
the MATRICS battery, with more modest evidence for such a
common factor in the CNTRACS tasks—supporting the view
that the CNTRACS tasks are more process specific than those
in the MATRICS battery (for reasons noted above). However,
to examine this more closely, the final goal of the present
study was to determine whether such a common factor of
shared variance correlates with functional capacity or observer
or self-reported function.
Method
Subjects
Subjects were recruited from five study sites: Washington
University in St. Louis (controls, n = 22; patients, n = 16),
Maryland Psychiatric Research Center at the University of
Maryland (controls, n = 25; patients, n = 20), University of
California–Davis (controls, n = 24; patients, n = 18), Rutgers
University (controls, n = 38; patients, n = 32), and University
of Minnesota–Twin Cities (controls, n = 23; patients, n = 18).
Each site received approval from their respective institutional
review boards, and all subjects signed an informed consent
document before beginning the study.
In total, 132 healthy control subjects and 104 schizophrenia
subjects participated in the study. All subjects were interviewed
164
Cogn Affect Behav Neurosci (2014) 14:161–174
using the Structured Clinical Interview for the Diagnostic and
Statistical Manual–IV (SCID; First, Spitzer, Gibbon, &
Williams, 1995). Individuals were excluded if they had experienced a serious head injury, endorsed a neurological disease,
or had a history of intellectual disability or other pervasive
developmental disorder. Subjects were also excluded if they
endorsed substance dependence in the last 6 months and/or
substance abuse in the past month. All subjects were native
English speakers and scored at least a six on the Wechsler Test
of Adult Reading (WTAR), a measure of premorbid IQ
(Weschler, 2001). All subjects received a drug and alcohol
screen on the day of testing.
In addition to the above criteria, healthy control subjects were
excluded if they had a history of schizophrenia, bipolar disorder,
or any other psychotic disorder, were currently experiencing
major depression, or were currently taking psychotropic or
cognition-enhancing medications. Patients were included only
if they met criteria for a diagnosis of schizophrenia or
schizoaffective disorder. In addition, patients were included only
if they did not anticipate any medication changes within a month
and had stable outpatient or partial hospital status. Demographic
characteristics for each group are shown in Table 1.
Procedure
All subjects were assessed across four study sessions. During
session one, subjects completed the SCID (First et al., 1995),
WTAR (Weschler, 2001), two cognitive measures from the
MATRICS battery, the UPSA–B (Mausbach, Harvey,
Goldman, Jeste, & Patterson, 2007), an alcohol screen via
breathalyzer, and a drug screen using a test card sensitive to
the presence of street drugs in a urine sample; patients completed three additional measures of functional outcome (the
Specific Levels of Functioning Scale–Self, the Specific Levels
of Functioning Scale–Informant, and the Multidimensional
Scale of Independent Functioning) (Jaeger, Berns, &
Czobor, 2003; Schneider & Struening, 1983). Session two,
which was planned within 2 weeks of session one, included an
alcohol and drug screen and five cognitive paradigms developed by the CNTRACS Consortium: the Dot Probe Expectancy Task (DPX), the AX-Continuous Performance Task
(AX-CPT), the Contrast-Contrast Effect (CCE), the Jittered
Orientation Visual Integration Task (JOVI), and the Relational
and Item-Specific Encoding Task (RISE), all of which
are described further below. Subjects also repeated the
CNTRACS tasks at two additional sessions to assess test–
retest reliability. However, the focus of the present study was
just on the baseline assessment. All subjects performed the
tasks in the order listed above, although the AX-CPT and DPX
tasks were counterbalanced, with one occurring at the beginning and the other at the end. Importantly, although the CCE
was administered to all subjects, our prior work suggested that
abnormal CCE performance could not be isolated from attention lapse errors (Barch et al., 2012) and that the critical CCE
Table 1 Demographic and clinical variables
Healthy Controls
Schizophrenia Patients
Variable
Age (years)
Gender (% males)
Race (% Caucasian)
Personal education (years)
Mean
38.3
53
48
14.0
SD
12.3
Personal SES
Father education (years)
Mother education (years)
Parental SES
WTAR
UPSA–B
MSIF Global
SLOF Self-Report
SLOF Informant Report
BPRS Positive Symptoms
BPRS Negative Symptoms
BPRS Disorganized Symptoms
BPRS Manic Symptoms
BPRS Depressed Symptoms
33.3
13.4
12.8
43.4
35.6
86.9
11.6
3.1
3.2
12.5
9.7
10.1
3.2
Mean
39.8
58
61
13.0
SD
11.9
24.1
13.6
13.5
46.1
32.9
76.7
3.7
130.4
123.5
2.2
1.8
1.3
1.2
1.9
8.8
3.4
2.9
12.8
10.2
14.6
1.3
13.9
17.4
1.3
0.72
0.47
0.35
0.85
3.9
Group Comparison
t = −0.95, p = 0.34
χ 2 = 0.51, p = 0.48
χ 2 = 0.06, p = 0.08
t = 2.2, p = 0.03
t
t
t
t
t
t
= 6.7, p < 0.001
= −0.57, p = 0.57
= −1.7, p = 0.09
= −1.6, p = 0.11
= 2.03, p = 0.04
= 6.3, p < 0.001
SES socioeconomic status, WTAR Wechsler Test of Adult Reading, UPSA–B, UCSD Performance-Based Skills Assessment–Brief, MSIF Multidimensional Scale of Independent Functioning, SLOF Specific Levels of Functioning Scale, BPRS, Brief Psychiatric Rating Scale
Cogn Affect Behav Neurosci (2014) 14:161–174
indices have relatively low reliability (Strauss et al., 2013).
Therefore, data from the CCE were not included in any of the
present analyses.
Measures of cognition
Performance was measured across three domains of cognitive
ability, using the four CNTRACS tasks mentioned above.
These computer-based paradigms have been previously reported in great detail, so only brief summaries of each task are
provided below. In addition, these tasks are openly available
to investigators at http://CNTRACS.ucdavis.edu/task.
1. Goal maintenance was assessed through the DPX paradigm (Henderson et al., 2012) and the letter-version of the
expectancy AX-CPT task (MacDonald, Pogue-Geile,
Johnson, & Carter, 2003; Schneider & Struening, 1983).
Both tasks require subjects to view a series of cue and
probe sequences, one stimulus at a time, and make a
buttonpress indicating whether the stimulus seen on the
screen does or does not complete a target stimulus pair. In
the AX-CPT version, this target pair is AX, meaning that
when a subject sees an A (cue) followed by an X (probe),
they should indicate that the X completes the target pair.
This is contrasted with a BX trial, during which an X
probe follows an invalid cue (“B,” which is any letter
other than “A”), so subjects must respond nontarget to X.
Critically, most trials (70%) are AX trials, creating two
expectations. The first is that an A cue will be followed by
an X probe, at least in people who use the cue to prepare
prospectively for upcoming probes. If so, subjects are
more likely to false alarm on “AY” trials (with “Y”
indicating any letter other than “X”) or to be slow to
respond correctly on “AY” trials. In addition, the second
expectation is that the majority of the X trials are target
probes (i.e., A followed by X), resulting in a response bias
for indicating that the X is a target. Thus, “BX” trials
require using the goal information provided by the “B”
cue to overcome this prepotent response tendency to respond target to “X.” This discrimination between AX and
BX trials therefore requires maintenance of task-relevant
goals. Accordingly, performance on both the DPX and
AX-CPT tasks are measured using d’context, a variable
that indexes the hit rate for AX trials, relative to false
alarms for BX trials. The procedure, numbers of trials of
each type, expectations, and interpretation of results are
identical for the DPX paradigm, with the only difference
being the stimuli. In the DPX paradigm, instead of
responding to letters, subjects must respond to dot arrays,
which are visual renderings of Braille versions of the AXCPT stimuli. Subjects are trained before beginning the task
to ensure that they can identify which dot array represents
the probe and which represents the cue for the target pair.
165
2. Visual integration was assessed using the JOVI
(Silverstein et al., 2012). On each trial of this task, a
display containing Gabor elements against a gray background is presented, with a subset of these elements
forming a leftward- or rightward-pointing oval (for
stimuli examples, see Silverstein et al., 2012). The subject’s task is to indicate on each trial, via a keypress,
whether the target stimulus is pointing to the right or left.
Task difficulty is manipulated by jittering the elements of
the contour by various degrees across several conditions.
At 0° jitter, a smooth shape is formed. As jitter increases
to ±7°, 9°, 11°, 13°, or 15° for each element of a single
contour, discrimination of the overall shape created by the
elements becomes increasingly difficult. Performance
was first characterized as accuracy (proportion correct)
for each individual jitter level, which was then fit to a
sigmoidal (cumulative logistic) function, which could
vary in shape along the parameters of threshold, slope,
and upper asymptote (Wichmann & Hill, 2001). Threshold
corresponds to the level of jitter at which a subject reaches
a level of accuracy that is halfway between the upper
asymptote (~100% correct) and chance (50%). Therefore,
the higher an individual’s threshold, the better he or she
was at visually integrating the shape at a higher jitter
magnitude. Threshold was used as the dependent variable
for this task.
3. Episodic memory encoding and retrieval was assessed
through the RISE, a task that includes two types of
encoding for visually presented objects: item-specific
and relational encoding (Ragland et al., 2012). During
item-specific encoding, subjects view 36 objects and must
decide whether the object is living or nonliving. During
relational encoding, subjects view 18 pairs of objects and
must decide whether one of the objects could fit inside of
the other object. Subjects then perform two different
retrieval tasks: item recognition and associative recognition. During item recognition, subjects view all previously
seen objects, mixed with an equal number of foils that are
similar (in color, size, etc.), and must determine whether
the object is old or new. During associative recognition,
subjects view pairs of objects, all of which are old but half
of which are foil pairs, and must decide whether those
objects had been previously paired together. Due to concerns about practice effects across the three administration
time points, three psychometrically similar versions of the
RISE were used with different stimuli. Performance on
this task was determined by overall recognition accuracy
(hit rate − false alarm rate) (1) for item recognition for
items following item-specific encoding (RISE IRIE), (2)
for item recognition for items following relational
encoding (RISE IRAE), and (3) for associative recognition for item pairs from the relational encoding condition
(RISE AR).
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In addition to the above paradigms from the CNTRACS
battery, subjects were also tested on two paradigms from the
MATRICS Consensus Cognitive Battery (MCCB): the SymbolCoding Test from the BACS (Keefe, Goldberg, Harvey, Gold,
Poe, & Coughenour, 2004), which is a MATRICS measure of
speed of processing, and the Hopkins Verbal Learning Task–
Revised (HVLT–R; Brandt, 1991), which is the MATRICS
measure of verbal learning and memory (Kern et al., 2011).
Although it would have been ideal to administer the entire
MCCB, time constraints would not allow it. As such, we selected the BACS Symbol Coding and HVLT–R, on the basis of
their large effect sizes for deficits in schizophrenia (Dickinson,
Ramsey, & Gold, 2007; Kern et al., 2011). Going forward, we
will use the acronym MATRICSsub when discussing the
BACS Symbol Coding and the HVLT–R, as a reminder that
not all MATRICS tasks were included in the present study. We
will also use the acronym BACSsc when discussing the
symbol-coding task from the BACS, as a reminder that only
this subtest from the BACS was used.
Functional capacity and status
To assess patients’ ability to perform everyday skills, we
administered the UPSA–B, which is a well-validated measure
requiring subjects to engage in simulated life skills (Patterson,
Goldman, McKibbin, Hughs, & Jeste, 2001; Twamley et al.,
2002). This measure is particularly useful because it does not
depend on self-report of perceived functioning and, instead,
tests these skills directly. We also included the Multidimensional Scale of Independent Functioning (MSIF; Jaeger et al.,
2003), which is a self-report scale that includes measures of
functioning within work, education, and residential environments, as well as assessing function in terms of the role
position the patients have within each environment, the level
of support they require, and their general performance. For the
purposes of the presented analyses, we used the global rating
of function, which combines scores from all of those domains.
Unlike the other outcome measures used, high scores on the
MSIF indicate poorer functioning. We also administered the
Specific Levels of Functioning Scale (SLOF; Schneider &
Struening, 1983), which measures the individual’s interpersonal relationships, participation in community activities, and
work skills. This scale was completed by the patients themselves; however, due to previous work showing that patient
reports alone may not provide a sufficiently valid assessment
of real-world function (Keefe, Poe, Walker, Kang, & Harvey,
2006), this scale was also filled out by an informant. The
informants included family members, case workers, and therapists who felt that they had sufficient knowledge to speak to
the patient’s functioning. This informant measure was
obtained from 71 of the 104 subjects with schizophrenia.
Finally, we used the WTAR (Weschler, 2001) as a measure
of premorbid IQ.
Cogn Affect Behav Neurosci (2014) 14:161–174
Clinical symptoms
All schizophrenia subjects were assessed using the 24-item
Brief Psychiatric Rating Scale (BPRS; Overall & Gorham,
1962)), which includes subscales for positive symptoms, negative symptoms, disorganized symptoms, manic symptoms,
and depressed mood. All items are measured on a 7-point
scale, ranging in severity from not present (1) to extremely
severe (7). Scores were derived through a semistructured
interview with a certified clinical rater. Total scores for each
subscale were calculated by summing all items within that
scale, and mean subscale scores were substituted for any
missing items. To ensure consistent ratings across sites, raters
were trained by teleconference, during which ratings and
anchor points for all scales were discussed and six training
videos were completed. For at least six interviews, raters had
to achieve agreement based on a “gold” standard (those of the
trainers who were highly skilled clinicians from either the St.
Louis or Maryland sites), in order to be certified. Agreement
was defined as no more than two items with a difference of
more than 1 rating point from the gold standard trainers. In
order to maintain reliability across sites throughout the study,
videotaped interviews were rated every 2–4 weeks, with all
raters participating in a teleconference to resolve discrepancies.
We conducted analyses on the relationship between task performance and clinical symptoms, as well as on the relationship
between functional capacity/status measures and clinical
symptoms. However, because these were not the focus of the
present study, they are presented in Supplemental Materials.
Data analysis
In order to assess relatedness of cognitive performance between the six different measures of the CNTRACS and the
two tasks from the MATRICS battery, bivariate intercorrelations were calculated, separately for each group. Correlations
with CNTRACS tasks, MATRICSsub tasks, measures of
functional outcome, and measures of symptom severity were
also calculated for the patient group. Correlations between
cognitive tasks and the four functional outcome measures
were corrected for multiple comparisons within each task
measure, with significance defined as p < 0.0125; corrections
were also made for correlations with the five symptom measures, with significance defined as p < 0.01.
To determine whether tasks from one battery accounted for
unique variance in schizophrenia impairments after accounting for performance on the other battery, we performed a
series of regressions. For the MATRICSsub tasks, we entered
BACSsc and HVLT–R as dependent variables (one regression
for each task) and used three core CNTRACS tasks (DPX,
JOVI, and RISE IRAE) as predictor variables. For predictor
variables we selected the DPX task (as opposed to the
AX-CPT), due to a recent study by our group showing
Cogn Affect Behav Neurosci (2014) 14:161–174
better reliability for the DPX (Strauss et al., 2013); we chose to
include the RISE IRAE variable (as opposed to the other RISE
variables) because it is the most specific variable for assessing
a relational processing deficit (Ragland et al., 2012). We then
saved the residuals, which quantified the amount of variance
remaining within the MATRICSsub tasks after accounting for
the variance shared by the CNTRACS tasks. This was then
done for each of the six CNTRACS tasks, but using the
BACSsc and HVLT–R as predictors. This process yielded
residual scores for each of the eight cognitive tasks, taking
into account the variance shared by the other cognitive battery.
These residual scores were then compared between the patient
and control groups in an independent samples t-test and were
also correlated with symptom and function measures.
To address the question of shared variance even more
conservatively, we performed another series of linear regressions in which variance shared by all other tasks was regressed
out and the residuals for each individual task were saved,
leaving only unique task variance (when predicting DPX, the
AX-CPT was not included as an independent variable, and the
other RISE measures were not used when predicting RISE).
These residuals were then compared between groups and also
correlated with functioning and symptom measures.
In addition, to understand how the common or shared variance itself related to function, we performed an unrotated principal axis factor analysis, again for all subjects, which included
the three core CNTRACS tasks, BACSsc, and HVLT–R. Only
factors with eigenvalues greater than one were retained, and
these factor scores were saved as variables and correlated with
symptom and function measures within the patient group.
Results
Demographic and clinical characteristics
Schizophrenia patients and healthy control subjects did not
significantly differ on age, gender, race, parental socioeconomic
status (SES), or parental education. Schizophrenia patients had
significantly lower personal education and personal SES, as
compared with healthy control subjects (see Table 1). Additionally, there were no significant differences in clinical or demographic variables between schizophrenia patients who did and
did not have a completed SLOF Informant report, except for a
trend toward higher maternal education for the patients who had
SLOF Informant data, t(96) = −1.99, p = 0.05.
What is the relationship among the CNTRACS tasks
and among the MATRICSsub tasks?
As is shown in Table 2, the structure of the correlations within
the patient group among the CNTRACS tasks was similar to
that in our prior study (Gold et al., 2012). Specifically, the
167
three RISE measures were significantly intercorrelated
(rs range from 0.32 to 0.91, p < 0.01). In addition the AXCPT and DPX measures were significantly intercorrelated,
r = 0.65, p < 0.001, and were also correlated with the RISE
measures (rs range from 0.25 to 0.39, p < 0.05). In contrast,
JOVI threshold was not correlated with any other task score.
The pattern of intercorrelations was broadly similar for the
RISE and AX-CPT/DPX tasks for healthy control subjects
(see Table 2), although correlation coefficients were generally
smaller. In controls, the JOVI showed modest correlations
with two of the RISE measures and the AX-CPT. From the
MATRICSsub, the HVLT–R and the BACSsc were correlated
in both the patients, r = 0.48, p < 0.001, and controls, r = 0.48,
p < 0.001.
What is the relationship between the CNTRACS
and MATRICSsub tasks?
As is shown in Table 3, the HVLT–R showed consistent correlations with both the AX-CPT/DPX and RISE measures for
both patients and controls, with little correlation with the JOVI.
The BACSsc also showed consistent correlations with the AXCPT/DPX and RISE, although the correlations were somewhat
weaker among patients for the RISE item recognition measures.
The BACSsc did show somewhat more evidence of association
with the JOVI than did the HVLT–R, although the magnitudes
of these correlations were not significantly different.
Do controls and patients differ on MATRICSsub, CNTRACS,
and function measures?
Group differences in CNTRACS task performance in this sample have been reported previously (Strauss et al., 2013). However, for clarity, we report group differences for the CNTRACS
tasks here as well. Schizophrenia patients performed significantly worse than healthy controls on all CNTRACS and
MATRICSsub tasks (Table 4). Average effect size for
CNTRACS tasks was 0.83 (range: Cohen’s d = 0.31–1.05),
and average effect size for the MATRICSsub tasks was 1.04
(BACSsc, d = 0.88; HVLT–R, d = 1.21). Schizophrenia patients
had a significantly lower premorbid IQ than did healthy controls, t(234) = 2.03, p = 0.044, and also had significantly poorer
functioning, as measured by the UPSA–B, t(233) = 6.3, p <
0.001. Patients who did and did not have completed SLOF
Informant data did not significantly differ in performance on
any cognitive task or on any other function measure.
Do controls and patients differ on MATRICSsub
or CNTRACS tasks after accounting for variance shared
by the other task battery?
As was described in the Method section, we calculated residual scores for each of the task measures, after regressing out
168
Cogn Affect Behav Neurosci (2014) 14:161–174
Table 2 Correlations among the CNTRACS tasks in controls and individuals with schizophrenia
RISE IRIE
RISE IRAE
RISE AR
AX-CPT D’-context
DPX D’-context
JOVI threshold
RISE IRIE
RISE IRAE
RISE AR
AX-CPT D’-Context
DPX D’-Context
JOVI Threshold
–
0.64**
0.42**
0.19*
0.25**
−0.14
0.91**
–
0.57**
0.13
0.09
−0.21*
0.32**
0.36**
–
0.23**
0.23*
−0.21*
0.39**
0.34**
0.34**
–
0.63**
−0.18*
0.30**
0.27*
0.25*
0.65**
–
−0.15
−0.14
−0.20
−0.18
−0.11
−0.10
–
Correlations for patients are above the diagonal (italics) and correlations for controls are below the diagonal. RISE Relational and Item-Specific
Encoding Task, IRIE item recognition for items following item-specific encoding, IRAE item recognition for items following relational encoding, AR
associative recognition for item pairs from the relational encoding condition, AX-CPT AX-Continuous Performance Task, DPX Dot Probe Expectancy
Task, JOVI Jittered Orientation Visual Integration Task
* p < 0.05
** p < 0.01
the variance shared by measures from the other battery. After
taking out variance shared with the BACSsc and HVLT–R, all
three RISE measures remained significantly different between
groups [RISE AR, t (224) = 3.11, p < 0.01; RISE IRAE,
t(224) = 4.51, p < 0.001; RISE IRIE, t(224) = 3.77, p <
0.001]. The DPX and AX-CPT also remained significant,
t(219) = 2.03, p < 0.05, and t(223) = 2.05, p < 0.05, respectively. However JOVI performance no longer differed between groups, t(216) = −0.85, p = 0.40. After accounting
for variance shared with the CNTRACS measures, BACSsc
and HVLT–R performance continued to be significantly
worse in patients than in controls, t(203) = 2.55, p = 0.01,
and t(203) = 3.9, p < 0.001, respectively.
Do controls and patients differ on MATRICSsub
or CNTRACS tasks after accounting for variance shared by all
other tasks, even on the same battery?
We next calculated residuals for each task, after including all other tasks as independent variables. We found
that performance for the RISE IRIE, t (202) = 3.20, p <
0.01, RISE IRAE, t (202) = 3.94, p < 0.001, RISE AR,
t (202) = 2.71, p < 0.01, and HVLT–R, t (202) = 2.82,
p < 0.01, continued to be significantly different between
patients and controls. The BACSsc, JOVI, DPX, and
AX-CPT residuals no longer showed significant group
differences.
Table 3 Correlations between the CNTRACS tasks, MATRICSsub tasks, and WTAR
Healthy Controls
RISE IRIE
RISE IRAE
RISE AR
AX-CPT D’-context
DPX D’-context
JOVI threshold
Schizophrenia Patients
HVLT–R
BACSsc
WTAR
HVLT–R
BACSsc
WTAR
0.19*
0.20*
0.26**
0.29**
0.34***
−0.20*
0.39***
0.44***
0.40***
0.32***
0.36***
−0.26**
0.41***
0.33***
0.39***
0.24**
0.22*
−0.14
0.28**
0.23*
0.37** *
0.36***
0.40***
−0.03
0.14
0.16
0.38***
0.31***
0.35***
−0.19
0.16
0.12
0.22*
0.23*
0.23*
−0.16
WTAR Wechsler Test of Adult Reading, HVLT–R Hopkins Verbal Learning Task–Revised, BACSsc Brief Assessment of Cognition in Schizophrenia,
RISE Relational and Item-Specific Encoding Task, IRIE item recognition for items following item-specific encoding item recognition for items
following item-specific encoding, IRAE item recognition for items following relational encoding, AR associative recognition for item pairs from the
relational encoding condition, AX-CPT AX-Continuous Performance Task, DPX Dot Probe Expectancy Task, JOVI Jittered Orientation Visual
Integration Task
* p < 0.05
** p < 0.01
*** p < 0.001
Cogn Affect Behav Neurosci (2014) 14:161–174
169
Table 4 Task performance in controls and individuals with schizophrenia
Healthy
Controls
RISE IRIE
RISE IRAE
RISE AR
AX-CPT D’-context
DPX D’-context
JOVI threshold
BACSsc
HVLT–R
0.84 (0.11)
0.83 (0.11)
0.56 (0.21)
3.3 (0.86)
3.2 (0.87)
1.1 (0.08)
57.5 (13.3)
25.8 (4.5)
Table 5 Functional and cognitive correlations for patients
Schizophrenia Group Comparison
Patients
0.65 (0.26)
0.62 (0.26)
0.34 (0.25)
1.6 (1.0)
2.3 (1.3)
1.2 (0.23)
46.7 (11.2)
20.0 (5.2)
t
t
t
t
= 7.5, p
= 8.3, p
= 7.5, p
= 6.2, p
< 0.001
< 0.001
< 0.001
< 0.001
t
t
t
t
= 6.5, p < 0.001
= −2.4, p = 0.02
= 9.2, p < 0.001
= 6.6, p < 0.001
RISE Relational and Item-Specific Encoding Task, IRIE item recognition
for items following item-specific encoding, IRAE item recognition for
items following relational encoding, AR associative recognition for item
pairs from the relational encoding condition, AX-CPT AX-Continuous
Performance Task, DPX Dot Probe Expectancy Task, JOVI Jittered
Orientation Visual Integration Task, BACSsc Brief Assessment of Cognition in Schizophrenia, HVLT–R Hopkins Verbal Learning Task–Revised
How well does cognitive performance predict functional
outcome?
Next, in our patient group, we performed correlations between
the cognitive measures and the measures of functional outcome (Table 5). Although all functional outcome assessments
were designed to measure related constructs, they themselves
were not all intercorrelated. The UPSA–B was significantly
associated only with the SLOF Informant report, r = 0.31, p =
0.01, the MSIF was significantly associated only with the
SLOF Patient report, r = −0.33, p = 0.001, and the SLOF
Patient and Informant reports were significantly associated
with each other, r = 0.31, p = 0.01.
We found that the UPSA–B was significantly correlated
with the AX-CPT, DPX, RISE IRAE, and BACSsc even after
corrections for multiple comparisons, with trends at a nominal
p -value of 0.05 for RISE IRIE, RISE AR, and HVLT–R.
MSIF Global scores were significantly correlated with the
HVLT–R, with a trend for the RISE IRAE. The SLOF Patient
report was significantly correlated with the HVLT–R. The
SLOF Informant report did not significantly correlate with
any of the tasks after correction for multiple comparisons.
However, the DPX was correlated with SLOF Informant
reports at a nominal p-value of 0.05.
Does cognitive performance continue to predict functional
outcome after accounting for variance shared by the other task
battery?
RISE IRIE
RISE IRAE
RISE AR
AX-CPT D’-context
DPX D’-context
JOVI Threshold
BACSsc
HVLT-R
UPSA–B
Total
MSIF
Global
SLOF
Patient
0.22*
0.30**
0.21*
0.25**
−0.23
−0.24*
0.05
−0.06
0.07
0.08
0.05
0.03
0.18
0.23
0.06
0.22
0.13
−0.10
0.17
0.28**
0.26*
0.04
0.01
0.08
0.29**
-0.20
0.30**
0.22*
−0.07
0.07
−0.10
−0.36***
SLOF
Informant
Bolded text indicates correlation coefficients that met significance after
correcting for multiple comparisons, whereas regular text with a single
asterisk indicates coefficients with only nominal (p < 0.05) significance.
UPSA–B UCSD Performance-Based Skills Assessment–Brief, MSIF
Multidimensional Scale of Independent Functioning, SLOF Specific
Levels of Functioning Scale, RISE Relational and Item-Specific
Encoding Task, IRIE item recognition for items following item-specific
encoding, IRAE item recognition for items following relational encoding,
AR associative recognition for item pairs from the relational encoding
condition, AX-CPT AX-Continuous Performance Task, DPX Dot Probe
Expectancy Task, JOVI Jittered Orientation Visual Integration Task,
BACSsc Brief Assessment of Cognition in Schizophrenia, HVLT–R
Hopkins Verbal Learning Task–Revised
* p < 0.05
** p < 0.0125
*** p < 0.001
scores on the UPSA–B, r = 0.27, p < 0.01, with trends for the
AX-CPT, r = 0.21, p < 0.05, and the RISE IRIE, r = 0.21, p <
0.05. The HVLT–R residual was significantly correlated with
the SLOF Patient report, r = 0.28, p < 0.01, and also trended
toward significance for the MSIF Global, r = −0.25, p = 0.02.
There were trends for the DPX, r = 0.24, p = 0.06, and the
RISE IRAE, r = 0.21, p = 0.10, residual to correlate with
SLOF Informant reports.
Does cognitive performance continue to predict functional
outcome after accounting for variance shared by all other
tasks?
When residual scores from regressing out all other tasks were
correlated with measures of functioning, we found that the
DPX, r = 0.26, p < 0.05, was still correlated with the UPSA–B
at a nominal p-value, although it did not pass corrections for
multiple comparisons. The residual for the HVLT–R remained
significantly correlated with scores on the MSIF, r = −0.33,
p < 0.01, and the SLOF Patient report, r = 0.29, p < 0.01.
Does common task variance predict functional outcome?
When residual scores from regressing out the alternative task
battery were correlated with measures of functioning, we
found that the DPX residual was significantly correlated with
The residual score analysis presented above indicates that there
was unique variance associated with both the CNTRACS tasks
170
and the MATRICSsub tasks that showed significant group
differences and relationships to measures of function. However, it is clear that there is also shared variance across the tasks,
and we wanted to understand how that shared variance related
to function. Thus, we performed a nonrotated principal axis
factor analysis that included the three CNTRACS tasks (DPX,
JOVI, and RISE IRAE) and both MATRICSsub tasks and
included both groups. The first factor, which was the only
factor with an eigenvalue of >1, explained 47% of the overall
variance. The four measures of “higher cognitive” functions
(DPX, RISE, HVLT–R, and BACSsc) loaded most strongly on
this factor (all > 0.56), and the JOVI, a measure of perceptual
integration, had a relatively low load (−0.28). This factor
correlated significantly with performance on the UPSA–B,
r = 0.48, p < 0.001), and trended toward significantly correlating with scores on the SLOF Informant report, r = 0.31, p =
0.02, and SLOF Patient report, r = 0.24, p = 0.03, after
correction for multiple comparisons.
Discussion
Overview
We assessed cognitive ability using paradigms put forth by
both the CNTRACS and MATRICS initiatives in a large
group of schizophrenia patients and healthy control subjects
and explored how cognitive ability related to functional outcome. For all cognitive paradigms, we evaluated the specificity of each task by examining how robustly it correlated with
other tasks designed to measure different cognitive functions.
In general, we found that a number of cognitive paradigms
from both the CNTRACS and MATRICSsub batteries were
intercorrelated at a somewhat higher level than in our prior
study (Gold et al., 2012). However, these intercorrelations
varied greatly, ranging from 0.03 to 0.91, with the highest
correlations for similar measures (e.g., DPX and AX-CPT)
and with the perceptual paradigm from the CNTRACS yielding the lowest intercorrelations with other tasks, which measured higher cognitive functions. In addition, we assessed the
contribution of both unique and common variance for each
task to group differences and relationships to function. A
number of the CNTRACS tasks, as well as the HVLT–R,
continued to identify group differences and to predict function
even after accounting for variance associated with other tasks.
Each of these results is discussed in more detail below.
Relationships among the CNTRACS tasks
We saw significant intercorrelations among many of the
CNTRACS tasks within both patients and controls. Importantly, as in our prior work (Gold et al., 2012), these intercorrelations were primarily between the RISE, DPX, and AX-CPT
Cogn Affect Behav Neurosci (2014) 14:161–174
tasks, which are all measures of higher cognitive functions
(episodic memory and goal maintenance) (Henderson et al.,
2012; Ragland et al., 2012). In contrast, the JOVI, which is a
task designed to measure the early perceptual function of visual
integration (Silverstein et al., 2012), was much more weakly
correlated with performance on these episodic memory and
goal maintenance tasks, in both patients and controls. This
pattern provides evidence for a distinction between perceptual
and high-level cognitive functions that is consistent with
the hypothesis of different neurobiological mechanisms
(Pylyshyn, 1999). Other support for this distinction comes
from prior research in which schizophrenia patients showed
a significant deficit on measures of contextual, but not perceptual, control, when compared with healthy control subjects
(Chambon et al., 2008). Given that cognitive control functions
supported by prefrontal cortex functions may contribute to
performance on both episodic encoding (Ranganath,
Minzenberg, & Ragland, 2008; Rizio & Dennis, 2013;
Spaniol et al., 2009) and goal maintenance tasks (for a
review, see Lesh et al., 2011), it is not surprising that the RISE
and AX-CPT/DPX tasks were significantly related and suggests that they share both common and unique processes.
While this pattern of intercorrelations is not surprising on
the basis of theories of cognitive control (Barch, Csernansky,
Conturo, & Snyder, 2002; Van Snellenberg, 2009), these
relationships were somewhat stronger than expected, given
our prior findings (Gold et al., 2012). In the first CNTRACS
study, we found minimal intercorrelations among the
CNTRACS tasks, such that the DPX did not correlate significantly with any other CNTRACS task (the AX-CPT was not
in the prior study), suggesting that each paradigm measured
fairly discrete cognitive processes. One possible contribution
to the stronger intertask correlations in the present study is a
difference in the design of the two studies. In the Gold et al.
study, subjects completed the tasks on different days, whereas
in the present study, all tasks were completed on the same day.
There are many variables that can affect performance on
cognitive tests, including nutrition (Dye, Lluch, & Blundell,
2000), mood (Ashby, Isen, & Turken, 1999), and fatigue
(Durmer & Dinges, 2005). If tested on the same day, these
factors would be likely to influence performance on tasks in a
consistent manner, thereby inducing some degree of correlation. In contrast, these state factors may not influence performance across tasks when testing subjects across multiple days.
Considering that most studies will test subjects only on a
single day, data from the present study are likely more reflective of the level of intercorrelation among CNTRACS tasks
that most investigators would observe.
Relationship between CNTRACS and MATRICSsub tasks
We felt that it was important to compare the relationships
between the CNTRACS and MATRICSsub tasks within the
Cogn Affect Behav Neurosci (2014) 14:161–174
same patient group, as well as their relative relationships to
measures of function. Interestingly, we found that both
MATRICSsub tasks were at least moderately correlated with
the RISE and AX-CPT/DPX measures from the CNTRACS,
although less strongly correlated (or not significantly correlated)
with performance on the JOVI. A relationship between the
HVLT–R and the RISE measures is consistent with both tasks
measuring aspects of episodic memory. The correlations between the HVLT–R and the AX-CPT/DPX are somewhat more
surprising. However, as was described above, cognitive control
functions can contribute to episodic memory performance. The
BACSsc is described as a measure of processing speed in the
MATRICSsub battery. However, good performance on the
BACSsc is facilitated by being able to maintain the symbols
and digit pairings in working memory and/or by memorizing
them via episodic memory function. In this framework, significant correlations between the BACSsc and the DPX/AX-CPT
and RISE are less surprising.
Common versus specific deficits
A pervasive topic in schizophrenia research is that of a generalized cognitive deficit, which is a hypothesized explanation
for the impairments observed across multiple cognitive domains in schizophrenia (Dickinson & Harvey, 2009;
Dickinson et al., 2008; Keefe, Bilder, Harvey, Davis,
Palmer, Gold & Lieberman, 2006a). When variability shared
by the alternative battery was removed, performance remained
significantly worse between patients and controls for all tasks
other than the JOVI, suggesting that impaired performance on
these tasks could not be explained solely by common variance
shared with the other cognitive battery. Taking this one step
further, we removed the variance shared by all other cognitive
tasks, leaving only unique task variance for each of the
CNTRACS and MATRICSsub paradigms. We continued to
find a significant deficit for the entire RISE measure and the
HVLT–R. It is interesting to note that the tasks continuing to
show group differences in unique variance are measures of
episodic memory, a domain previously hypothesized to be one
of the core cognitive deficits in schizophrenia (Hill, Beers,
Kmiec, Keshavan, & Sweeney, 2004; Saykin et al., 1991). It
should also be noted, however, that scores on measures of
visual integration, including the JOVI (Silverstein & Keane,
2009; Uhlhaas, Phillips, & Silverstein, 2005), as well as those
on some other perceptual tasks (Keane, Silverstein, Wang, &
Papathomas, 2013; Silverstein et al., 2013), are highly sensitive to clinical state, with normalization of performance occurring as patients move from the acute to stabilization to
stable phases of illness. Since our sample consisted of clinically stable patients who were all past the acute phase, we may
not have captured the potential for impaired visual integration
as well as we did performance impairment on other tasks that
measure impairments that are less state related.
171
These data again support the hypotheses that both common
and specific deficits are present in schizophrenia (Fornito
et al., 2011). Furthermore, these findings suggest that the
CNTRACS and MATRICSsub batteries measure both unique
and common cognitive deficits, with the percentage of shared
variance (47%) similar to that seen in previous studies of
cognition in schizophrenia (Dickinson et al., 2004; Keefe
et al., 2006b). Although the generalized or common deficit
is sometimes discussed as it if it were a “nuisance” variable, in
the present study it is picking up on important variance related
to function in schizophrenia, since the common variance was
strongly correlated with multiple measures of function. It is
interesting to speculate on the process represented by this
common deficit. It has been suggested that this common factor
might reflect “processing speed,” which could be contributing
to performance in many domains (Schatz, 1998). The fact that
the BACSsc loaded highly on the principal component is
consistent with this hypothesis. However, all four of the tasks
measuring “higher cognitive” functions loaded highly on this
factor, raising the possibility that part of this common deficit
reflects impairments in cognitive control that may cut across a
number of different domains. The latter account might bring
new psychological and neurobiological specificity to the concept of a general cognitive deficit in schizophrenia, since
cognitive control is made up of a well-characterized set of
cognitive processes linked to the function of a common
frontal-parietal neural network that is recruited when task
demands increase across a wide range of cognitive domains.
Associations with function
All of the cognitive tasks were moderately correlated with the
UPSA–B. The BACSsc, DPX, AX-CPT, and RISE IRAE
showed the strongest relationship with the UPSA–B. In addition, we found that, after regressing out the MATRICSsub
tasks, the DPX residual continued to significantly correlate
with the UPSA–B, and the HVLT–R residual correlated with
the MSIF and SLOF Patient report after regressing out the
CNTRACS tasks. These findings suggest that there is unique
variance associated with each task battery that predicts function, although possibly different aspects of functioning for the
CNTRACS and the MATRICSsub batteries. The CNTRACS
task residuals correlated with the UPSA–B, which is a
performance-based measure of functioning, as well as showing trends to correlate with observer rating measures of function (i.e., the DPX at a nominally significant p-value). Thus,
these data demonstrate that paradigms translated from basic
cognitive neuroscience meaningfully predict variance in functional capacity. The MATRICSsub task residuals correlated
with the MSIF and SLOF patient, which are measurements
based on self-report. However, comparing the correlation
coefficients of the MATRICSsub tasks and functional outcome measures that we obtained with those reported in other
172
studies, we notice some variations. For example, Nuechterlein
et al. (2008) found a weaker relationship between the HVLT–
R and self-reported functioning (rs ranged from −0.06 for
social functioning to 0.20 for independent living, with r =
0.14 for the composite), while Keefe et al. (2006a) and Burton
et al. (2013) reported slightly higher correlation coefficients
between the HVLT–R, BACSsc, and UPSA–B (rs > 0.30). It
is difficult to determine why these effect sizes vary across
studies, particularly given that most studies using the MCCB
report relationships only between functional outcome and the
composite score, not individual tasks, providing few comparisons. Perhaps importantly, within our sample, scores on the
MSIF and SLOF patient report were not significantly correlated with scores on the UPSA–B, suggesting that these
performance-based and report-based tools measure slightly
different aspects of functioning, which is, in turn, reflected
in their differential association with cognitive tasks. Although
this does not address why UPSA–B and self-report measures
have different relationships with tasks across studies, it does
help explain why studies have found distinct relationships
between cognitive tasks, performance-based functioning,
and self-reported functioning.
Limitations
We had hoped that the SLOF Informant report would provide
an externally assessed picture of patient functioning. However, we were unable to obtain the report for every subject.
Additionally, the informants who provided data were primarily family members, case workers, and therapists. There is
evidence that ratings from this range of informants are less
associated with patient behavior than are reports from only
high-contact clinicians, which were not available for all subjects in our study (Sabbag et al., 2011). This may have limited
our ability to detect a relationship between functioning and
cognitive performance, using the SLOF. Additionally, our
patient group was relatively high functioning, which limits
the generalizability of our results and also could have limited
our ability to detect relationships with functional outcome and
clinical symptom measures. Finally, as in most studies of
schizophrenia patients, our patient sample was medicated.
The impact of medication on cognitive performance is still
largely unclear, but it is important to note that this could have
impacted our data.
Conclusions
We observed a pattern of significant intercorrelations between
cognitive tasks from both the CNTRACS and MATRICS
initiatives, in both schizophrenia and healthy control subjects.
These intercorrelations were strongest among tasks that measured higher cognitive functions, such as memory, processing
Cogn Affect Behav Neurosci (2014) 14:161–174
speed, and goal maintenance, providing evidence for a shared
cognitive factor contributing to performance in both groups
and also providing evidence that a measure of early visual
perception is subserved by different processes. Ongoing tasks
using fMRI and the CNTRACS tasks in schizophrenia patients and controls will shed light on the nature and neural
underpinnings of these common factors and allow us to test
the hypothesis that it may be related to neural circuitry
supporting cognitive control. Further analysis of a common
deficit revealed unique task variance that remained sensitive
to group differences across all higher-order cognitive tasks,
although a more conservative analysis showed that unique
variance was most robustly related to group differences for
tasks measuring episodic memory. In line with the goal of
both the CNTRACS and MATRICS initiatives, we found
significant relationships between all cognitive tasks and measures of functional outcome. Notably, CNTRACS tasks were
more related to a performance-based and observer-rated measure of function, while MATRICSsub tasks were more related
to self-reported functioning, suggesting that tasks from these
batteries are picking up on slightly different aspects of the
measurement of functional outcome. Most important, our data
indicate that measures derived from the cognitive neuroscience
literature can show meaningful relationships to functional capacity and status and can identify deficits among individuals
with schizophrenia even when variance associated with a
common deficit is removed.
Acknowledgements We thank the staff at each of the CNTRACS sites
for their hard work and our subjects for their time, energy, and cooperation. This research was supported by National Institutes of Health (NIH)
grants MH084840, MH084826, MH084828, MH084861, MH08482,
and MH059883.
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